BOOST: Bootstrapping Strategy-Driven Reasoning Programs for Program-Guided Fact-Checking

2025-04-03

AI Summary

Complex claim verification suffers from insufficient strategic guidance and diversity in reasoning program generation, alongside difficulties in transitioning from zero-shot to few-shot settings. Method: This paper proposes the first fully automated, human-intervention-free bootstrapping few-shot framework. It explicitly models claim decomposition and information retrieval strategies as structured guidance, integrating program-guided reasoning, strategy-driven program synthesis, bootstrapped demonstration optimization, and execution-based evaluation. Contributions/Results: (1) It enables end-to-end automatic evolution of reasoning programs from zero-shot to few-shot regimes; (2) it significantly improves verification accuracy on complex claims, outperforming existing few-shot baselines; and (3) it enhances interpretability of the reasoning process and cross-domain generalization capability. The framework eliminates manual annotation or prompt engineering, achieving robust performance across diverse claim types and domains through self-improved demonstrations and strategy-aware program generation.

Technology Category

• Knowledge Representation and Reasoning: Automated Reasoning and Theorem Proving
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Program-guided reasoning has shown promise in complex claim fact-checking by decomposing claims into function calls and executing reasoning programs. However, prior work primarily relies on few-shot in-context learning (ICL) with ad-hoc demonstrations, which limit program diversity and require manual design with substantial domain knowledge. Fundamentally, the underlying principles of effective reasoning program generation still remain underexplored, making it challenging to construct effective demonstrations. To address this, we propose BOOST, a bootstrapping-based framework for few-shot reasoning program generation. BOOST explicitly integrates claim decomposition and information-gathering strategies as structural guidance for program generation, iteratively refining bootstrapped demonstrations in a strategy-driven and data-centric manner without human intervention. This enables a seamless transition from zero-shot to few-shot strategic program-guided learning, enhancing interpretability and effectiveness. Experimental results show that BOOST outperforms prior few-shot baselines in both zero-shot and few-shot settings for complex claim verification.

Problem

• Enhancing program diversity in reasoning-based fact-checking
• Reducing manual design dependency for reasoning programs
• Improving zero-shot to few-shot strategic program learning

Innovation

• Bootstrapping-based framework for program generation
• Integrates claim decomposition and information-gathering strategies
• Iteratively refines demonstrations without human intervention